How Much Will AI Affect Critical Thinking?

At the risk of seeming flippant, artificial intelligence probably will not negatively affect “critical thinking” very much. 

The reason has less to do with “AI giving one the answers” and more the reality that critical thinking skills likely will remain a bell curve. Though controversial for social reasons, bell curves are widely found in nature.  

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Also, such skills are domain specific, researchers suggest. People reason using their knowledge of a particular subject matter, and skills learned in one domain do not automatically transfer to another, researchers suggest. 

not a capability that “most students” automatically acquire to a high level simply by going through school or college. Programs in school meant to teach critical thinking skills have had limited success, some suggest. 

Richard Arum and Josipa Roksa's longitudinal Collegiate Learning Assessment project, which followed more than 2,300 students at 24 four-year institutions, found that 45 percent of students showed no statistically significant improvement in critical thinking, complex reasoning and written communication during their first two years of college.

And the pattern of skill improvements might well suggest that preexisting conditions (skills, aptitude, experience) shape the extent of skills acquisition. 

Another study found some changes after four years of college work, though generally of “small” dimensions. The average amount of improvement is modest, and the distribution of outcomes is wide, which is about what a bell curve distribution might suggest would be the case. 

In other words, the causality might be that low critical thinking skills lead to high AI dependence, rather than high AI dependence leading to lower critical thinking.

Student type	Pre-AI ability	Likely AI-era behavior
Low reasoning	Low	Accepts plausible answers with little checking
Lower-middle	Moderate	Uses AI effectively for routine tasks but misses subtle errors
Middle	Moderate	Can check obvious errors, struggles with sophisticated ones
Upper-middle	High	Uses AI iteratively and verifies important claims
High	Very high	Challenges AI, generates counterarguments, identifies subtle errors
Expert	Very high + domain knowledge	Uses AI as an intellectual multiplier

A 2024 study suggests some over-reliance and reduced independent decision-making, critical thinking and analytical reasoning among students using AI. Lower cognitive proficiency was found in another study.  

But there probably is not a single bell curve of "critical thinking." There could be separate distributions for different capabilities such as reasoning, knowledge, metacognition, skepticism, evidence evaluation and domain expertise.

Critical thinking might involve some combination of:

• identifying assumptions

• evaluating evidence

• distinguishing correlation from causation

• recognizing alternative explanations

• detecting logical errors

• comparing competing interpretations

• asking appropriate questions

• judging the reliability of information

• drawing justified conclusions

• revising one's beliefs when evidence changes.

But most of us do not use such skills, most of the time, for most daily interactions. In fact, such reasoning might be fairly rare, in real life, for most people, most of the time. 

The unsettled issue is whether, and to what extent, AI will change the shape of the cognitive skills curve. In principle, AI could flatten the curve, if it raises all learner capabilities. But it might also accentuate differences if AI is differentially useful. In other words, the curve might be reshaped.

But such a distribution might also be unusual in nature. If preexisting skills also tend to predict post-AI skill levels, then a standard bell distribution might be the result. 

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Agentic AI Will Reshape Web Ad Economics

“For at least the last at least 30 years, the business model of the internet has been advertising,” says Matthew Prince, Cloudflare CEO. “It’s not the entire business model of the internet, but it’s really driven all of the growth of the web.”

So what happens now that artificial intelligence traffic for training, inference and agentic operations begins to dominate web traffic?

Already, automated traffic has now passed human traffic. “Five years from now, we think that automated traffic will be 1,000 times human traffic,” he says. 

“The challenge of that is, if you have 1,000 times more traffic, someone’s got to pay for the infrastructure to power that,” says Prince. “That’s going to require bandwidth, that’s going to require servers, that’s going to require a lot of things.”

The traditional model of how to pay for that, which was advertising, doesn’t work for bots because they do not click on ads, which destroys the monetization mechanism. 

So the issue is how content providers will create new revenue mechanisms for bot traffic, since ads do not work. 

For that matter, it is not clear how subscription or commerce revenues will be affected, either. 

Traditional web	AI/agentic web
Human is the "customer"	Human may never visit
Page view creates advertising opportunity	Bot request may create no ad impression
Search crawler is economically valuable because it sends traffic	AI crawler can consume content without sending traffic
More traffic generally = more revenue	More bot traffic can = more bandwidth/compute/security cost
SEO means getting a high search ranking	AEO means getting selected/cited by an AI
Affiliate click produces revenue	AI agent may bypass the affiliate link
E-commerce wants customer on its site	Agent may choose product and potentially transact elsewhere
Content is given away in exchange for distribution	Content increasingly becomes a licensable input

All that suggests we might have to invent new ways of generating revenue beyond advertising, almost all of which might involve some form of payment for content. 

Model	How it works	Economic logic
Annual licensing	AI company pays publisher fixed fee	Similar to syndication
Pay-per-crawl	Payment for each page/request	Metered consumption
Pay-per-answer	Payment when content contributes to an answer	Closer to value created
Revenue share	Publisher gets share of AI subscription/ad revenue	Aligns incentives
Referral/affiliate	AI sends user to publisher	Preserves old model
Transaction fee	Website earns money when agent completes transaction	Potentially much larger
API access	AI accesses structured proprietary data	Turns website into data provider

It remains to be seen whether licensing regimes can replace lost advertising revenues, though. Commerce revenues should help, but it is not unreasonable to suggest the new business models might not be as lucrative as the older ad-based models. 

As we have seen in other businesses disrupted by the internet, such as music, subscriptions and events might become more important. In most other cases, it is easier to see how agentic commerce revenues might well be a bigger opportunity. 

Web firm type	Old primary economic engine	AI-era pressure	Likely new revenue
News publisher	Ads + subscriptions	AI answers substitute for clicks	AI licenses + subscriptions + events
Reference/data site	Ads	AI extracts information	Data/API licensing
UGC platform	Ads + engagement	AI absorbs user-generated knowledge	AI licensing + transactions
E-commerce	Product margin + ads	AI becomes shopping interface	Agent transactions + APIs + sponsored placement
Travel site	Ads + booking commissions	Agent bypasses comparison site	Agent booking commissions
Review site	Ads + affiliate	AI summarizes reviews	Licensing + affiliate/transaction fees
SaaS/web app	Subscription	Agent performs tasks without UI	API/agent usage fees
Search engine	Advertising	AI answer reduces external clicks	AI advertising + transactions
Social platform	Ads	AI consumes content without users	Licensing + commerce
Cloud/CDN/security provider	Infrastructure fees	Huge AI bot volume	Bot management + AI traffic infrastructure
Marketplace	Seller fees/ads	Agent becomes buyer interface	Transaction fees + agent APIs

And to the extent that advertising value shifts, it might shift in the direction of payments that optimize a supplier’s visibility in the candidate set or actual purchasing behavior. When an agent is searching hotels in a city with certain requirements, payment might take the form of paid placements to enhance inclusion, ranking, then selection and booking. 

Previously the scarce asset was supplying an audience. In the agentic AI era, value might shift to  proprietary information, trusted data, transaction capability and permission to act.

That might be an easier transition for commerce-oriented sellers, compared to content suppliers dependent on human visitors and advertising.

Lots of Reasons to Worry about AI Infra, But Customer Concentration Cannot be Helped

There are lots of reasons for investors to worry about the artificial intelligence infrastructure business. After all, firms generally are committing capital to infrastructure faster than the economic value of AI is becoming visible. 

The investment question is therefore whether AI demand will grow fast enough, and profitably enough, to absorb the enormous quantity of infrastructure being financed today.

Concern	What investors worry about	Why AI infrastructure is particularly exposed	Potential consequence
Overbuilding / excess capacity	Infrastructure is being built on expectations of enormous future AI demand that may not materialize at the projected rate	Data centers and GPUs are highly capital-intensive and difficult to redeploy	Falling utilization, lower rental rates and impaired asset values
Return on invested capital	AI revenue may grow rapidly but still fail to produce returns commensurate with the enormous capital investment	A $1 billion data center requires substantial utilization for years to earn an attractive return	Lower ROIC and pressure on hyperscaler valuations
Rapid technology obsolescence	Today's expensive GPU cluster may become economically inferior before its expected useful life ends	AI accelerators are advancing unusually rapidly	Accelerated depreciation and residual-value losses
Falling AI compute prices	Competition and increasingly efficient models could cause the price of compute to fall faster than infrastructure costs	Compute is becoming increasingly commoditized	Infrastructure operators could experience margin compression
Financing leverage	The industry increasingly needs debt, leases, project finance, guarantees and SPVs to fund expansion	Capital requirements have grown faster than many companies' internally generated cash	Credit deterioration and refinancing risk
Off-balance-sheet exposure	Some economic obligations may be less obvious than conventional corporate debt	Guarantees, leases, joint ventures and residual-value guarantees complicate analysis	Investors discover greater effective leverage during a downturn
Customer concentration	A data center may effectively depend on one or two giant AI customers	New facilities are enormous and frequently built around anchor tenants	Default or spending cuts by one customer can undermine project economics
Circular financing / ecosystem dependency	Suppliers, customers and financiers may increasingly finance one another	Nvidia, hyperscalers, AI labs, data-center developers and private capital are becoming financially intertwined	Difficulty determining how much demand is truly independent
Power constraints	Infrastructure may be valuable but unusable because electricity cannot be delivered on schedule	AI facilities require enormous amounts of concentrated power	Delays, stranded land/capacity and higher costs
Permitting and community opposition	Local governments or residents may delay or block projects	AI data centers have unusually large power, water, land and noise footprints	Construction delays and unexpected costs
Grid / energy-price risk	Electricity may become substantially more expensive as AI competes for scarce generation	Power is becoming a major component of AI operating costs	Lower data-center margins and higher customer prices
Demand concentration in a few companies	Much of the spending ultimately depends on a handful of hyperscalers and frontier-model companies continuing to spend aggressively	Microsoft, Amazon, Google, Meta, Oracle and a small number of AI labs dominate demand	A capex slowdown could propagate rapidly through the supply chain
AI monetization uncertainty	AI usage may grow enormously without generating enough incremental profits to justify infrastructure spending	Consumers and businesses increasingly expect AI to become cheap or bundled into existing products	Revenue growth can lag infrastructure investment
Macro/interest-rate sensitivity	Higher rates make long-duration infrastructure investments less attractive	Data centers have large upfront costs and long payback periods	Lower valuations and more expensive project financing
Regulatory/geopolitical risk	Export controls, AI regulation, energy policy or restrictions on data centers could change demand	AI infrastructure is concentrated in a relatively small number of countries and suppliers	Assets may become stranded or economically less valuable

But one of the concerns is almost unavoidable. Standard business strategy is to diversify customer bases so that no change at any single customer account will imperil the business overall. But that seems virtually impossible in the AI infrastructure business, especially the “compute as a service” segment. 

The reason is that there simply are very few major buyers. 

Computing Product Category	Dominant Buyer Segment	Market Dynamics & Demand Concentration Drivers
High-Performance AI Accelerators (GPUs/TPUs)	Microsoft, Meta, Google, Amazon (AWS)	Hyperscalers routinely absorb upwards of 40-50% of total advanced enterprise chip allocations, deploying hundreds of thousands of specialized accelerators per cluster to train and serve frontier models.
Custom AI Silicon & ASICs	Google (TPUs), Amazon (Trainium/Inferentia), Meta (MTIA), Microsoft (Maia)	Demand is entirely insourced and consolidated among the top cloud operators designing proprietary chips to reduce dependency on merchant silicon and optimize workload economics.
Enterprise Server Racks & Motherboards	Hyperscale Data Center Operators & Large Cloud Providers	Traditional enterprise server buyers are bypassed by custom Open Compute Project (OCP) specs ordered at massive scale by a handful of operators, leaving original design manufacturers (ODMs) highly concentrated.
High-Bandwidth Memory (HBM)	Major GPU Makers fulfilling Hyperscaler Orders	Production capacity for advanced multi-layer memory stacks is heavily pre-committed to fulfill multi-billion dollar buildouts driven by the major cloud titans.
Advanced Data Center Networking (InfiniBand / High-Speed Ethernet Switches)	Microsoft, Meta, Google, Amazon, Oracle	Ultra-low latency fabric requirements limit early-stage adoption of cutting-edge networking gear to the major cloud providers scaling distributed GPU training fabrics.

But that is not unique to the AI infrastructure business. Some infrastructure markets are inherently oligopsonistic (there may be many suppliers, but only a handful of economically viable buyers).

TSMC doesn't have the option of saying, "If Apple doesn't order this next-generation process, we'll simply find 100 other customers." The universe of customers capable of economically using a 2-nanometer-class process is tiny: Apple, Nvidia, AMD, Qualcomm, Broadcom, MediaTek and a handful of others.

Some other industries also have very-concentrated buyers. In the commercial aerospace business, tier-one avionics and structural component suppliers sell almost exclusively to a duopoly of global aircraft manufacturers: Boeing and Airbus.

In the defense contracting industry, specialized aerospace, radar, and cybersecurity firms rely almost entirely on a single primary buyer: the U.S. Department of Defense or allied national governments.

In the advanced semiconductor equipment industry, companies producing critical lithography systems sell to a handful of chip fabrication giants such as TSMC, Samsung, and Intel.

In railway rolling stock, manufacturers of heavy locomotives and specialized railcars interface with heavily consolidated markets dominated by national freight networks or state-run transit authorities.

Market	Typical number of economically meaningful buyers	Why buyers are so few	Examples of suppliers
AI hyperscale data centers	~5–10	Enormous power, capital and computing requirements mean only hyperscalers and a few AI companies can consume large campuses	Nvidia, Credo, Arista Networks, Cisco, HPE
Advanced semiconductor fabs	~5–10 major customers	Leading-edge chips require enormous volumes and only a few companies design them	TSMC, Samsung, Intel Foundry
Extreme-ultraviolet lithography	Essentially 2–3	Only a handful of semiconductor manufacturers need leading-edge EUV equipment	ASML
Commercial aircraft	~50–100 significant airlines globally, but much smaller number of major customers	Aircraft cost hundreds of millions of dollars and fleet procurement is concentrated	Boeing, Airbus
Large commercial jet engines	~10–20 major airline/airframe customers	Few aircraft platforms and three major engine manufacturers	GE Aerospace, RTX/Pratt & Whitney, Rolls-Royce
Cruise ships	~10–20 major cruise operators	Extremely expensive specialized vessels; only major cruise companies can order them	Meyer Werft, Fincantieri, Chantiers
Nuclear reactors	Very few utilities/developers	Gigawatt-scale projects cost billions and require specialized sites, regulation and financing	GE Vernova, Westinghouse, EDF, KHNP
Military aircraft	A handful of governments	National-security requirements restrict buyers; export markets are politically constrained	Lockheed Martin, Boeing, RTX, Northrop Grumman
Military satellites / launch systems	Very few governments	Classified requirements and national-security restrictions sharply constrain customers	Lockheed Martin, Northrop, Boeing, SpaceX
Large LNG projects / LNG carriers	Relatively few global buyers	Huge projects require long-term contracts and specialized infrastructure	Cheniere, QatarEnergy, shipyards
Electricity generation equipment	Hundreds of utilities, but few very large buyers	Large turbines and grid equipment are bought in relatively infrequent, lumpy projects	GE Vernova, Siemens Energy, Mitsubishi Heavy
High-end semiconductor packaging	Relatively few hyperscalers/chip designers	Advanced packaging is expensive and tied to particular chip architectures	TSMC, ASE, Amkor
Mining equipment for ultra-large mines	A few dozen major mining companies	Huge trucks, shovels and autonomous systems are economically viable mainly at enormous mines	Caterpillar, Komatsu, Epiroc
Offshore oil platforms / FPSOs	A few dozen oil companies	Projects cost billions and are geographically and technically specialized	SBM Offshore, MODEC, Samsung Heavy
High-speed rail equipment	Few national/state buyers	Large projects are government-controlled and geographically constrained	Alstom, Siemens Mobility, CRRC
Container ships	Many shipping companies, but highly concentrated among largest carriers	Very large vessels require substantial capital and are ordered in batches	Hyundai, Hanwha Ocean, CSSC

There are many reasons investors can worry about the health of the AI infrastructure business. But customer concentration does not seem a concern that can realistically be avoided. Some industries are just like that: there are few potential buyers.